SIGNAL-PROCESSING BASED ON PARAMETER STRUCTURAL MODELING AND SEPARATION OF HIGHLY CORRELATED SIGNALS OF KNOWN STRUCTURE

Results in the study of signal processing based on the use of paramete r structural modeling (PSM) are presented. First, we introduce a speci al form of time-series modeling based on signal-dependent building blo cks. Such modeling is used in the design of a nested-form transversal structure, known as a composite filter, based on a shift-invariant fin ite impulse resonse (FIR) as well as infinite impulse response (IIR) b uilding blocks. The newly proposed composite PSM model (CPSM) possesse s a unique feature, namely, its ability to suppress one signal of a gi ven structure, while at the same time being ideally transparent to ano ther one. The intrinsic property of this proposed CPSM is its enhanced insensitivity with respect to noise as well as its ability to fast tr ack, in contrast to the commonly used linear line-enhancer based on co nventional autoregressive moving average (ARMA), thus leading to a mor e practically sound processing of short-duration signals. It is shown that the proposed time-series modeling based on CPSM can be effectivel y applied towards the separation of superimposed signals of heavily ov erlapping spectra. Next, the parameter-invariant nonlinear structural signal representation based on shift-invariant CPSM is presented. The use of this model in the design of annihilation operators (AO) is desc ribed, and composite parameter-free structural modeling (CPFSM) is dev eloped. Based on this model, two canonical forms of the parameter-inva riant null filters (PINF) are presented, and their use in the suppress ion of a given class of signals, independently of the values of their a priori unknown parameters, is illustrated. The paper also presents s ome simulation examples illustrating the application of the proposed C PSM and CPFSM in solving problems of detection and parameter estimatio n in the presence of highly non-Gaussian, mainly signal-like interfere nces.